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Microsoft SQL Server 2008 R2 Unleashed- P213

Microsoft SQL Server 2008 R2 Unleashed- P213:SQL Server 2005 provided a number of significant new features and enhancements over what was available in SQL Server 2000. This is not too surprising considering there was a five-year gap between these major releases.Microsoft SQL Server 2008 is not as much of a quantum leap forward from SQL Server 2005 | 2084 CHAPTER 51 SQL Server 2008 Analysis Services FIGURE 51.57 Creating KPIs in the cube designer. previously known. As you create dimensions you can even choose a data mining model as the basis for a dimension. Basically a data mining model is a reference structure that represents the grouping and predictive analysis of relational or multidimensional data. It is composed of rules patterns and other statistical information of the data that it was analyzing. These are called cases. A case set is simply a means for viewing the physical data. Different case sets can be constructed from the same physical data. Basically a case is defined from a particular point of view. If the algorithm you are using supports the view you can use mining models to make predictions based on these findings. Another aspect of a data mining model is using training data. This process determines the relative importance of each attribute in a data mining model. It does this by recursively partitioning data into smaller groups until no more splitting can occur. During this partitioning process information is gathered from the attributes used to determine the split. Probability can be established for each categorization of data in these splits. This type of data can be used to help determine factors about other data utilizing these probabilities. This training data in the form of dimensions levels member properties and measures is used to process the OLAP data mining model and further define the data mining column structure for the case set. In SSAS Microsoft provides several data mining algorithms or techniques Association Rules This algorithm builds rules that describe which items are most likely to appear together in a transaction. The rules help predict when the presence of one item is likely with another item which has appeared in the same type of transaction before . Clustering This algorithm uses iterative techniques to group records from a dataset into clusters that contain similar .